Data QA Engineer: ETL, BigQuery & HealthTech

ТРТ; United States; Remote; USD 90,000 - 120,000; Full time

Benefits offered by this job

Unlimited PTO

Company hardware after probation

Paid New Year's holidays

Job summary

ТРТ is seeking a mid-level Data QA Engineer to validate ETL data loads and ensure BigQuery and Spanner datasets are accurate, complete, and aligned with business rules. You will verify source-to-target reconciliation, data transformation logic, and data freshness, while collaborating with data engineers on root-cause analysis.

The role emphasizes data quality, risk reduction, and robust test documentation, with an eye toward PHI handling and CI/CD integration in a dynamic cloud environment.

Qualifications

• Experience in data QA/testing data-intensive systems.
• Strong SQL skills and data validation expertise.
• Ability to perform source-to-target reconciliation and trace data issues.
• Experience with regression, smoke, and exploratory testing.
• Knowledge of Agile methodologies and bug reporting practices.

Responsibilities

• Test ETL/ELT data loads into BigQuery and Spanner from multiple sources.
• Run data quality checks and validate consistency, integrity, and business rules.
• Investigate data discrepancies and trace to source, load, or transformation steps.
• Collaborate with data engineers to perform root-cause analysis and verify fixes.
• Contribute to CI/CD by integrating data tests into pipelines.
• Create and maintain test cases and documentation.
• Avoid exposing PHI in tickets and logs, and review AI-generated outputs.

Skills

SQL

Data QA

Data validation

Agile

Analytical thinking

Communication

Tools

Zephyr

TestRail

Jira

Git

Airflow

dbt

Job description

ТРТ is seeking a mid-level Data QA Engineer to validate ETL data loads and ensure BigQuery and Spanner datasets are accurate, complete, and aligned with business rules. You will verify source-to-target reconciliation, data transformation logic, and data freshness, while collaborating with data engineers on root-cause analysis.

The role emphasizes data quality, risk reduction, and robust test documentation, with an eye toward PHI handling and CI/CD integration in a dynamic cloud environment.